Frame interpolation creates an image frame from neighboring images. The neighboring images may be fields in an interlaced video format, used to form a frame of data, or neighboring frames of a soon-to-be-created frame. Higher frame rates are generally desirable.
In the simplest approach, one could increase the frame rate by repeating the most recent frame until the next frame is ready for display. However, this does not account for moving objects which may appear to jump from frame to frame and have flickering artifacts instead of the appearance of smooth motion.
Motion estimation and motion compensation techniques may alleviate many of these issues. These techniques rely upon motion vectors to shift the image data for the moving object to the correct position in interpolated frames, thereby compensating for the motion of the object and allowing for better accuracy in interpolated frames.
In addition, motion compensation is used in converting from two-dimensional (2D) to three-dimensional (3D) images. This is generally accomplished using a depth map in conjunction with the motion estimation. More accurate motion estimation allows for a more accurate conversion and a better resulting image.
Issues arise with computational complexity and the use of resources in motion compensation and motion estimation. Techniques that accomplish motion compensation with high accuracy for relatively low computational costs and overall lower hardware costs would be beneficial.